voom {limma} | R Documentation |
Transform count data to log2-counts per million, estimate the mean-variance relationship and use this to compute appropriate observational-level weights. The data are then ready for linear modelling.
voom(counts, design = NULL, lib.size = NULL, normalize.method = "none", plot = FALSE, ...)
counts |
either a numeric |
design |
design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates. |
lib.size |
numeric vector containing total librazy sizes for each sample. If |
normalize.method |
normalization method to be applied to the log2-counts-per-million. Choices are as for the |
plot |
|
... |
other arguments are passed to |
This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
voom
is an acronym for mean-variance modelling at the observational level.
The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
Count data almost show non-trivial mean-variance relationships.
Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
The weights are then used in the linear modelling process to adjust for heteroscedasticity.
In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess
. The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. Tags with zero counts across all samples are not included in the lowess fit.
Optional normalization is performed using normalizeBetweenArrays
.
Using fitted values of log2 counts from a linear model fit by lmFit
, variances from the mean-variance trend were interpolated for each observation. This was carried out by approxfun
. Inverse variance weights can be used to correct for mean-variance trend in the count data.
An EList object with the following components:
E |
numeric matrix of normalized expression values on the log2 scale |
weights |
numeric matrix of inverse variance weights |
design |
numeric matrix of experimental design |
lib.size |
numeric vector of total library sizes |
genes |
dataframe of gene annotation, only if |
Charity Law and Gordon Smyth